Inference pipeline system and method

ABSTRACT

A system to infer place data is disclosed that receives location data collected on a user&#39;s mobile electronic device, recognizes when, where and for how long the user makes stops, generates possible places visited, and predicts the likelihood of a user to visit those places.

CROSS-REFERENCE TO RELATED APPLICATION

This application is related to U.S. application Ser. No. 13/405,182filed concurrently, entitled “SYSTEM AND METHOD FOR DATA COLLECTION TOVALIDATE LOCATION DATA” which is fully incorporated by reference.

BACKGROUND

There are a variety of existing technologies which track and monitorlocation data. One example is a Global Positioning Satellite (GPS)system which captures location information at regular intervals fromearth-orbiting satellites. Another example is a radio frequencyidentification (RFID) system which identifies and tracks the location ofassets and inventory by affixing a small microchip or tag to an objector person being tracked. Tracking of individuals, devices and goods maybe performed using WiFi (IEEE 802.11), cellular wireless (2G, 3G, 4G,etc.) and other WLAN, WAN and other wireless communicationstechnologies.

Additional technologies exist which use geographical positioning toprovide information or entertainment services based on a user'slocation. In one example, an individual uses a mobile device to identifythe nearest ATM or restaurant based on his or her current location.Another example is the delivery of targeted advertising or promotions toindividuals whom are near a particular eating or retail establishment.

In existing systems, received information, such as both user data andplace data are noisy. User location data can be noisy due to poor GPSreception, poor Wi-Fi reception, or weak cell phone signals. Similarly,mobile electronic devices can lack certain types of sensors or have lowquality sensor readings. In the same way, the absence of a comprehensivedatabase of places with sufficient coverage and accurate locationinformation causes place data to also be noisy.

The need exists for a method that utilizes location data to accuratelyidentify the location of people, objects, goods, etc., as well asprovide additional benefits. Overall, the examples herein of some prioror related systems and their associated limitations are intended to beillustrative and not exclusive. Other limitations of existing or priorsystems will be become apparent to those skilled in the art upon readingthe following Detailed Description.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Further features of theinvention, its nature and various advantages, will be apparent from thefollowing detailed description and drawings, and from the claims.

Examples of a system and method for a data collection system areillustrated in the figures. The examples and figures are illustrativerather than limiting.

FIG. 1 depicts an example of an inference pipeline system and anenvironment in which one embodiment of the inference pipeline system canoperate.

FIG. 2 depicts a high-level view of the workflow of the inferencepipeline.

FIG. 3 illustrates an example path of a user and the subsequent datathat can be derived and analyzed by the inference pipeline system.

FIG. 4 depicts a block diagram of example components in an embodiment ofthe analytics server of the inference pipeline system.

FIGS. 5A and 5B depict example inputs and outputs to a clusteringalgorithm.

FIG. 6 depicts a location trace based method of detecting movement.

FIG. 7 depicts an example presentation of a personal location profile.

DETAILED DESCRIPTION

An inference pipeline system and method which incorporates validatedlocation data into inference models is described herein. Given a user'sinformation collected from a mobile electronic device, the inferencepipeline recognizes whether a user visited a place, and if so, theprobability of the user at a place, and how much time the user spent atthe place. It also produces user location profiles, which includeinformation about familiar routes and places.

In some cases, the inference pipeline systems and methods are part of alarger platform for identifying and monitoring a user's location. Forexample, the inference pipeline system can be coupled to a datacollection system which collects and validates location data from amobile device. Collected user information includes location data such aslatitude, longitude, or altitude determinations, sensor data from, forexample, compass/bearing data, accelerometer or gyroscope measurements,and other information that can be used to help identify a user'slocation and activity. Additional details of the data collection systemcan be found in U.S. patent application Ser. No. 13/405,182.

A place includes any physical establishment such as a restaurant, apark, a grocery store, or a gas station. Places can share the same name.For example, a Starbucks café in one block and a Starbucks café in adifferent block are separate places. Places can also share the sameaddress. For example, a book store and the coffee shop inside areseparate places. Each place can have attributes which include streetaddress, category, hours of operation, customer reviews, popularity, andother information.

In one embodiment, the inference pipeline recognizes when a user visitsa place based on location and sensor data. As an example, the inferencepipeline system recognizes when a user makes a stop. Next, the placewhere the user has stopped can be predicted by searching various datasources, combining signals such as place attributes, collecting datafrom a mobile electronic device, harvesting user demographic and userprofile information, monitoring external factors such as season,weather, and events, and using an inference model to generate theprobabilities of a user visiting a place.

In another embodiment, the inference pipeline combines various signalsto rank all the possible places a user could be visiting. In anotherembodiment, the inference pipeline estimates the probability of a uservisiting a place and the time user has spent at a place.

Various examples of the invention will now be described. The followingdescription provides certain specific details for a thoroughunderstanding and enabling description of these examples. One skilled inthe relevant technology will understand, however, that the invention maybe practiced without many of these details. Likewise, one skilled in therelevant technology will also understand that the invention may includemany other obvious features not described in detail herein.Additionally, some well-known structures or functions may not be shownor described in detail below, to avoid unnecessarily obscuring therelevant descriptions of the various examples.

The terminology used below is to be interpreted in its broadestreasonable manner, even though it is being used in conjunction with adetailed description of certain specific examples of the invention.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection.

FIG. 1 and the following discussion provide a brief, general descriptionof a representative environment 100 in which an inference pipelinesystem 120 can operate. A user device 102 is shown which moves from onelocation to another. As an example, user device 102 moves from alocation A 104 to location B 106 to location C 108. The user device 102may be any suitable device for sending and receiving communications andmay represent various electronic systems, such as personal computers,laptop computers, tablet computers, mobile phones, mobile gamingdevices, or the like. Those skilled in the relevant art will appreciatethat aspects of the invention can be practiced with othercommunications, data processing, or computer system configurations,including: Internet appliances, hand-held devices [including personaldigital assistants (PDAs)], wearable computers, all manner of cellularor mobile phones [including Voice over IP (VoIP) phones], dumbterminals, media players, gaming devices, multi-processor systems,microprocessor-based or programmable consumer electronics, and the like.

As the user device 102 changes locations, the inference pipeline system120 receives location information through a communication network 110.Network 110 is capable of providing wireless communications using anysuitable short-range or long-range communications protocol (e.g., awireless communications infrastructure including communications towersand telecommunications servers). In other embodiments, network 110 maysupport Wi-Fi (e.g., 802.11 protocol), Bluetooth, high-frequency systems(e.g., 2G/3G/4G, 900 MHz, 2.4 GHz, and 5.6 GHz communication systems),infrared, or other relatively localized wireless communication protocol,or any combination thereof. As such, any suitable circuitry, device,and/or system operative to create a communications network may be usedto create network 110. In some embodiments, network 110 supportsprotocols used by wireless and cellular phones. Such protocols mayinclude, for example, GSM, GSM plus EDGE, CDMA, quad-band, and othercellular protocols. Network 110 also supports long range communicationprotocols (e.g., Wi-Fi) and protocols for placing and receiving callsusing VoIP or LAN.

As will be described in additional detail herein, the inference pipelinesystem 120 comprises of an analytics server 122 coupled to a database124. Indeed, the terms “system.” “platform,” “server,” “host,”“infrastructure,” and the like are generally used interchangeablyherein, and may refer to any computing device or system or any dataprocessor.

1. Input/Output

This section describes inputs and outputs of the inference pipeline.

1.1 Input

The input to the inference pipeline is a sequence of location and/orsensor readings that have been logged by the mobile electronic device.For example, the data may come from GPS, Wi-Fi networks, cell phonetriangulation, sensor networks, other indoor or outdoor positioningtechnologies, sensors in the device itself or embedded in a user's bodyor belongings, and geo-tagged contents such as photos and text.

For example, for location data from GPS, each location reading includesa time stamp, location source, latitude, longitude, altitude, accuracyestimation, bearing and speed. Each sensor reading includes a timestamp, type of sensor, and values.

The frequency of location and sensor readings depends on how the user istracked (e.g., continuously-tracked, session-based).

1.2 User Data Acquisition

Data may be acquired from the user through various methods. In oneembodiment, there are two user data acquisition methods. The first is bycontinuously tracking users whom have a tracking application installedand running at all times. For these users, locations are logged with alow frequency to conserve battery life, such as once per minute.

The second method is session-based whereby users are indirectly trackedthrough third-parties. When to start and end tracking is controlled bythe third-party application or device. When a tracking session begins, auser location is logged with a high frequency, to compensate forpotentially short usage time. As an example, table 1 provides exampleinput data to the inference pipeline, such as location readings.

Table 1 shows accuracy values which are sometimes available fromlocation providers. For example, a device with the Android operatingsystem produces accuracy estimations in meters for GPS, WiFi, andcell-tower triangulated locations. For GPS, accuracy estimations can bewithin 50 meters while cell-phone tower triangulations have accuracyestimations within 1500 meters. In the example shown in Table 1, thehigher the accuracy value, the less accurate the reading.

TABLE 1 Time Latitude Longitude Altitude Accuracy Fri, 22 Jul 2011−28.167926 153.528904 48.599998 19 01:39:18 GMT Fri, 22 Jul 2011−28.167920 153.528890 45.700001 17 01:39:19 GMT Fri, 22 Jul 2011−28.167922 153.528866 47.700001 15 01:39:20 GMT1.3 Output

The output of the inference pipeline is a list of places that the useris predicted to have visited. Each predicted place includes place name,place address, start time of the visit and end time of the visit. Theinference pipeline system includes maintaining a place database as willbe discussed herein. Thus, each prediction also has an identifier to theplace entry in the database so that other information about the place isaccessible. As an example, Table 2 provides example output data from theinference pipeline, such as place predictions.

TABLE 2 From To Place Mon, 07 Nov 2011 Mon, 07 Nov 2011 Valley Curls19:18:32 GMT 20:02:21 GMT Mon, 07 Nov 2011 Mon, 07 Nov 2011 MesquiteCity 20:17:24 GMT 20:24:11 GMT Hall2. Workflow

FIG. 2 is a high-level view 200 of the workflow of the inferencepipeline. The pipeline takes raw location and sensor readings as inputand generates probabilities that a user has visited a place.

For each data acquisition mode (i.e., continuously-tracked, andsession-based), different techniques are used to predict the location ofthe user. This section focuses on the first type, continuously trackedusers. The other type, session users, will be discussed later.

First the location readings, ordered by time stamp, are passed to atemporal clustering algorithm that produces a list of location clusters.Each cluster consists of a number of location readings that arechronologically continuous and geographically close to each other. Theexistence of a cluster indicates that the user was relatively stationaryduring a certain period of time. For example, if a user stopped by aShell gas station from 8:30 AM to 8:40 AM, drove to a Starbucks coffeeat 9:00 AM, and left the coffee shop at 9:20 AM, ideally two clustersshould be generated from the location readings in this time period. Thefirst cluster is made up of a number of location readings between 8:30AM and 8:40 AM, and those locations should be very close to the actuallocation of the gas station. The second cluster is made up of a numberof location readings between 9:00 AM and 9:20 AM, and those locationsshould be very close to the coffee shop. Any location readings between8:40 AM and 9:00 AM are not used for inference. Each cluster has acentroid that the system computes by combining the locations of thiscluster. A cluster can be further segmented into a number ofsub-clusters, each with a centroid that is called a sub-centroid.

After a cluster is identified from a user's location readings, the placedatabase is queried for places nearby the cluster's centroid. Thissearch uses a large radius in hope to mitigate the noise in locationdata and cover all the candidate places the user may be located. Afeature generation process examines each candidate place and extractsfeatures that characterize the place. The inference model takes thefeatures of each candidate place and generates the probabilities of eachcandidate being the correct place.

To tune this inference model, a “ground truth,” or process to confirm ormore accurately determine place location, is created that includesmultiple mappings from location readings to places. A machine learningmodule uses this ground truth as training and testing data set to finetune the model.

3. Process

FIG. 4 depicts a block diagram of example components or modules in anembodiment of the analytics server 122 of the inference pipeline. Asshown in FIG. 4, the analytics server 122 of the inference pipeline caninclude, but is not limited to, a clustering and analysis component 202,a filtering component, 204, a movement classifier component 206,segmentation component 208, a merging component 210, and a stopclassifier component 212. (Use of the term “system” herein may refer tosome or all of the elements of FIG. 3, or other aspects of the inferencepipeline system 120.) The following describes details of each individualcomponent.

The functional units described in this specification may be calledcomponents or modules, in order to more particularly emphasize theirimplementation independence. The components/modules may also beimplemented in software for execution by various types of processors. Anidentified module of executable code may, for instance, comprise one ormore physical or logical blocks of computer instructions which may, forinstance, be organized as an object, procedure, or function.Nevertheless, the executables of an identified module need not bephysically located together, but may comprise disparate instructionsstored in different locations which, when joined logically together,comprise the module and achieve the stated purpose for the module.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

3.1 Clustering

The clustering and analysis module 202 takes raw location data andsensor data as input and detects when a user has visited a place, andhow a user transitions from one place to another place. The clusteringmodule may include three basic processes. First, raw location data passthrough a filter to remove spiky location readings. Second, the stop andmovement classifier identify user movement, and segmentation splits thelocation sequence into segments during which the user is believed to bestationary or stopped. Third, neighboring segments that are at the samelocation are merged to further reduce the effect of location noise.

3.1.1 Filtering

The filtering component 204 may filter two types of location readings:location readings with low accuracies and noisy location readings.

The first type, location readings with low accuracies, can includelocation data from cell tower triangulation, which may be filtered out.Any other location data with estimated accuracy worse than a thresholdcan be filtered out too. This accuracy estimation is reported by themobile electronic device. As described above, accuracy estimations aremeasured in meters and a reasonable threshold would be 50 meters.

The second type, noisy location readings, can be locations withoutexplicitly low accuracy estimations, but have specific attributes (e.g.,unreliable, erratic, etc.). To capture spiky locations, a window sizemay be used to measure the deviation of a location reading. Apredetermined number of location readings immediately preceding thelocation in question, and a predetermined number of location readingsimmediately after, are used to compute the average location of aneighborhood (e.g., two readings before and after). If the distancebetween location in question and the neighborhood average is greaterthan a threshold, the location in question is removed. In this case, thethreshold can be different from the threshold for accuracy estimationsand is used to prevent spiky location readings that are reported to behighly accurate. As an example, a WiFi location may have a high accuracyvalue (e.g., low number in meters), but in fact be pinpointing a placein a different country.

FIGS. 5A and 5B illustrate example inputs and outputs of a clustering202 module or process. FIG. 5A shows the various location readings whileFIG. 5B shows the final results of the clustering module 202. X-axis istime, while y-axes are latitude and longitude. The input is a sequenceof location readings that can be very noisy. After running theclustering module, three clusters are identified from the straight linesshown in FIG. 5B, with centroids at coordinates <47.613, −122.333>,<47.616, −122.355>, and <47.611, −122.331> respectively. Although FIGS.5A and 5B only show latitude and longitude, altitude and sensor data mayalso be taken into account in the clustering module.

3.1.2 Movement Classifier

The movement classifier component 206 can detect movement in at leasttwo ways under the clustering module 202.

3.1.2.1 Location Trace Based

Under location trace based methods, the movement classifier component206 uses a sliding time window that moves along a location sequence. Foreach window, the movement classifier component determines whether theuser moved during this period. If the movement classifier componentdetermines that the user has likely moved, the classifier splits thelocation sequence at the point in the window where the user speed isgreatest.

FIG. 6 illustrates this location trace based method 600 of detectingmovement. As illustrated in FIG. 6, each block in the five rowsrepresents a location reading. The second and third windows 620 and 630are classified as moving and the other windows 640, 650, 660 areclassified as not moving. As the result, the location sequence is brokeninto two segments as shown by the row 670.

The sliding window in the example of FIG. 6 has a size of six readings.The movement classifier uses this window of six readings to determine ifthe user was moving. First the diameter of the bounding box of thiswindow is computed using the minimum and maximum of latitude andlongitude.diameter=D(<latitude_(min),longitude_(min)>,<latitude_(max),longitude_(max)>)where D is the great-circle distance between two locations on Earth.

Additionally, the speed of this window, defined below, is also computed

${speed} = \frac{diameter}{duration}$where duration is the length of the sliding window.

If the diameter is greater than a threshold, such as 100 meters, or ifthe speed is greater than a threshold (such as one meter per second),the classifier outputs true (moving); otherwise it outputs false (notmoving).

3.1.2.2 Sensor Based Method

The other method uses sensors in the tracking device to detect if a useris moving. When accelerometer data is available, the movement classifieruses a similar sliding window and applies a Fourier transform to thesensor data (e.g., accelerometer data) to calculate the base frequencyof user movement in each time window. Depending on this frequency, auser's movement is classified as moving or stationary.

3.1.3 Splitting Location Sequence

If the movement classifier classifies a window as moving, the classifieridentifies a point of maximal speed and splits the location sequence atthat point.

If the window is classified as not moving, the sliding window shifts onelocation reading towards the future, and the process repeats.

3.1.4 Segmentation and Centroid Computation

After the sliding window has covered all the locations of a user, thesegmentation component 208 divides the whole location sequence into oneor more segments. Each segment is called a location cluster. Thesegmentation component then calculates the centroid of a locationcluster by combining the locations in this cluster.

${latitude}^{*} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{w_{i}{latitude}_{i}}}}$${longitude}^{*} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{w_{i}{longitude}_{i}}}}$where w_(i) is the weight for the i^(th) location. The weighted valuedepends on the source of the location. As an example, GPS locations havehigher weights than other locations. In another implementation, aninternet search engine may be weighted higher than a restaurantrecommendation service.3.1.5 Merging

It is possible an actual cluster is broken into several clusters,because of noisy location data. An extra step is taken or a processimplemented to merge consecutive clusters if their centroids are close.The merging component 210 handles such merging. One condition,implemented by the merging component, for two neighboring clusters tomerge is when the distance is below a threshold, and the start time ofthe later cluster is no later than a few minutes after the end of theearlier cluster.

3.1.6 Stop Classifier

The stop classifier component 212 examines the duration of each cluster,and decides if the cluster represents a user staying at a place. If theduration does not fall into a predefined range, the cluster is filteredout and not considered for next stages. An example predefined rangewould be any time between two minutes and three hours.

3.2 Candidate Search

After clusters are generated under the clustering module 202, the systemsends search requests or queries to a place database to retrieve placesclose to each cluster's centroid.

The place database contains information for all the places that havebeen collected by the analytics server. The information includes name,address, location, category, business hours, reviews, and parcelgeometries. The place database is geospatially indexed so that a searchby location can be quickly performed. All places that are within aradius from the cluster centroid are returned as candidate places.

Place data is generated from multiple sources, including by not limitedto, geographic information databases, government databases, review Websites, social networks, and online maps. Places from multiple sourcesmay be matched and merged, creating a richer representation of eachphysical place. By combining information from multiple sources, one canminimize noise and improve data quality. For example, averaginglocations of the same place from all sources, and giving higher weightsto more reliable sources can improve the estimation of the actuallocation of the place. The actual location of each place is provided bya place location ground truth (described later).

3.3 Feature Generation

For each cluster, the analytics server of the inference pipeline takesall of the place candidates found from the place database and ranks themby the probability that a typical user, or this particular user, wouldvisit. Before that, features are extracted from each candidate.

Three types of features can be extracted from each place candidate:

-   -   1. Cluster features are only dependent on the cluster itself,        not any place candidates. A cluster feature has the same value        for all the candidates of a cluster.    -   2. Place features are place specific features. Each candidate        may have different place features depending on their attributes        such as locations and categories.    -   3. User features which depend on the user's profile such as        demographic attributes. Similar to cluster features, user        features are the same for all the candidates of a cluster.        3.3.1 Cluster Features

Cluster features describe properties of the cluster and can include:

-   -   Duration of the cluster    -   Noisiness of location readings in the cluster, measured as the        average accuracy    -   Number of location readings in the cluster    -   Radius of the cluster    -   Probability of visiting different categories of places given the        timestamp for the cluster or “cluster time”    -   Density of places near the cluster centroid    -   Zoning information of the cluster centroid    -   Season, weather, or temperature associated with the cluster time        3.3.2 Place Features

Place features describe properties of a place candidate. Place featuresinclude

-   -   Distance to cluster centroid    -   Number of sources: The place database collects place information        from a number of reverse geo-coding sources. This feature counts        how many data sources provides the place    -   Low quality place location: Some data sources have low accuracy        place locations. This feature examines if a place candidate only        comes from individual or lower accuracy data sources.    -   Popularity: Popularity of a place can be calculated from number        of times users check in to a place, number of people who        connected to a place on social networks, number of comments or        reviews, Wi-Fi/Bluetooth signal visibility to users' mobile        electronic device, noise level, transaction volumes, sales        numbers, and user visits captured by other sensors.    -   Category: If the category taxonomy is a multi-level hierarchy,        more than one features can be used    -   Single user place: This feature is defined as whether a place        has small number of users who ever checked in or otherwise        visited this location.    -   Review count: For places that come from data sources with        reviews, this feature is the total number of reviews    -   Time-category match: This feature measures the probability of a        user visiting a place of a particular category, given the day or        time of day. For example, at 7 AM, a user is more likely to        visit a coffee shop than a night club    -   Business hours: Probability the place is open at the time of the        visit.    -   Parcel distance: The distance between the centroid of the parcel        and the centroid of the cluster    -   Cluster in place parcel: This feature is true when the centroid        of the cluster falls into the parcel of the place        3.3.3 User Features

User features are generated from user profile, including demographics,mobile device information, user location history and other informationthat helps predict where a user is visiting. Past place visits are alsouseful in predicting future visits. For example, if high-confidenceinferences have been made in the past that a user visited a certainneighborhood grocery store every week, the user is more likely to go tothis place if he/she hasn't been there for seven days.

-   -   User demographics, including age, gender, income level,        ethnicity, education, marital status, and number of children    -   Distance to home location    -   Distance to work location    -   Commute patterns    -   Device features including manufacturer, model, service provider,        and sensor availability.    -   Frequently visited places in the past.        3.4 Inference Engine

All the features are combined as a composite feature vector andnormalized to the range of zero to one. For each cluster, the inferenceengine examines the feature vectors of each candidate place and ranksthem by the probability of being the actual place.

3.4.1 Ranking Candidates

The inference engine may use a mapping from a feature vector F to aranking score s, such as via a linear model:

$\begin{matrix}{s = {{W^{T}F} - b}} \\{= {{\sum\limits_{i = 1}^{M}{W_{i}F_{i}}} - b}}\end{matrix}$where W is the weight vector, b is the bias value, and M is the totalnumber of candidates. The higher this ranking score, the more likely thecandidate is the actually place. The weight vector is determined by thetraining process.3.4.2 Probability Estimation

The inference engine then turns the score into a probability

ϕ = e^(s)${P( {Place}_{j} )} = \frac{\phi_{j}}{\sum\limits_{k = 1}^{n}\phi_{k}}$With this probability, one can either take the candidate with thehighest probability as the inference output, or use a list of topcandidates, along with their probabilities, as the result.3.5 Ground Truth Generation

In order to verify inference models, the system collects ground truthdata. This data is considered to be accurate observations that link auser with a place at an instance of time. This data is used to train andvalidate the inference models downstream. Some sources of reference datainclude, but are not limited to, the following:

3.5.1 Place Location

Ground truth for place locations is collected by having human labelersassociate places with locations on mapping tools. Each location is thenconverted to a latitude-longitude coordinate and stored in a database.The labelers may be tasked with identifying the place in question on anaerial map and writing down the coordinates of the center of thebuilding and the geometry of the building. If there is more than oneplace sharing the same building, the best estimation of the correctcorner is taken.

3.5.2 Place Confirmation

This is a place where a user checks in or confirms as the actual placeassociated with his or her location. If a place is not listed, the userhas the ability to manually add the name of the place. A placeconfirmation includes data sources such as a voluntary check-in, or fromsensor measurements of temperature, sound, light, proximity sensors, ornear field communication (NFC) chipsets, etc.

Place confirmations are determined with a separate tool that collectsground truth data. An example of such tool is a mobile application thatsuggests a list of places to check-in to, given the user's location.Every time a user checks-in to a place, this is logged as a check-inobservation event. When the check-in is registered, the check-inobservation event is associated with a latitude and longitude andtimestamp. This source of data serves as a direct validation ofinference model and is used to train the model.

3.5.3 Offline Check-In

Users can check in to a place via third party location-based services.When a user registers a check-in, the analytics server requests check-ininformation logged by the user within that service. These check-ins aretransmitted as offline check-ins. This source of data serves asreference data for the inference pipeline. By combining mobile devicedata and user check-in created using third-party software, the analyticsserver can map a user's device data to places.

3.5.4 Place Queries

A check-in service includes social networking websites that allow usersto “check in” to a physical place and share their location with othermembers. Examples of check-in services include Facebook, Foursquare,Google Latitude, and Gowalla. Check-in services provide a list of nearbyplaces and allow an opportunity for the user to confirm a place s/he hasvisited. The place data can then be stored with the check-in serviceprovider in order to support personal analytics, sharing on socialnetworks, earning incentives, public and private recognition, generalmeasurement, broadcasting, and history. Every time a user queriescheck-in services for nearby places, the data is packaged into anobservation that is sent to the analytics servers. A place querycontains a time stamp, user location, and places received from eachcheck-in service. This data helps to preserve and recover original usercheck-in experience for verification purpose.

3.5.5 Place Survey Answers

This is obtained from sources that include but is not limited toresponses to survey questions relating to places visited by a user.Survey questions are generated by the inference pipeline and serve as aform of feedback mechanism. In one implementation, survey questions aregenerated automatically. After the inference pipeline predicts a user'svisits within a certain time window, an algorithm evaluates theuncertainty of each inferred visit and picks highly-uncertain instancesto generate survey questions. Questions are generated using variousrules to improve the accuracy of an inference, and eliminate resultsthat are highly ambiguous or have a low confidence. In turn, uncertaininferences are used to generate survey questions because the inferencestend to have low accuracies and thus, any validation of such inferenceshelps improve the inference engine. An example presentation of surveyquestions is

“Were you at one of the following places around 4 PM yesterday?”

-   -   A. Starbucks Coffee    -   B. McDonald's    -   C. Wal-Mart    -   D. None of the above

Answers to Survey Questions are used as training data to fine-tune theinference model, and improve the chance that the correct place is rankedas top candidate. User data from mobile devices and survey questionanswers are paired up to create training samples, and input to machinelearning algorithm to adjust the inference model.

To detect fraud and estimate data quality, random places are included insurvey questions. In one implementation, a place that is not in thevicinity of the user is added to the choices. If this place is picked,the survey question is marked as invalid. Statistics associated withfraudulent answers are used to determine the reliability of surveyanswers. Also, the order of answers is randomized to remove positionbias.

3.5.6 Activity Journal

By combining a digital journal with a mobile device, a user can beassociated with a verified location in order to produce validated placedata. The device registers observations that include location data, andthe digital journal enables an individual to log actual place and timedata. The digital journal which includes place and time data is combinedwith device observation using time stamp as the “join point.” A joinpoint can use time to correlate digital log data with device observationdata. This joining of two sources of data generates reference data forthe inference pipeline. For example, when a user enter information intoa digital log that he was at a coffee shop at 9 AM, and when there isobservation data collected at 9 AM, a join point is created for 9 AMthat associates the device observations with a location of the user(i.e., coffee shop).

Journals are tabular data in any form, such as a spreadsheet ordigitized paper form. Processing journals can either be a manual orautomatic process. Journals can be turned into any format, such as textfiles or a database, as long as it can be processed by the inferencepipeline.

Reference data is considered highly accurate and is designed to generatepinpoint accuracy in terms of location, place, time, and activity.

TABLE 3 Actions at Place Name Mall/Area Date Walk-In Time Leave TimeEntry/Exit Path to Place Place Notes/Issues General Main St Nov. 8, 201111:52 am 12:01 pm South Door Took sidewalk Stood near NA Store fromparking entrance lot

Table 3 provides example fields in the digital log completed by ajournal-taker. The journal-taker completes each of the columnsconfirming the place name of a visit records any optional notes orissues. The time stamp can be automatically filled-in or manuallyentered. The values in these fields assign information to deviceobservation data. The level of precision and accuracy is considered highbased on the details collected by the digital logs. This data isconsidered high value across reference data sources.

3.5.7 Third-Party Reference Data

Data can be bulk imported into the system from third-party feeds thatinclude questionnaires, travel logs, credit card purchase logs, mobilewallet transactions, point-of-sale (POS) transactions, bar codescanning, security card or electronic access data (e.g., access tobuildings, garages, etc. using security cards/PIN codes), etc. Theinference pipeline infrastructure provides interfaces to bulk importdata that can augment the database with reference data.

3.5.7.1 User-Generated Contents

Validation data can be generated by associating data collected on mobiledevices with user-generated contents on social networks, review sitesand other types of information sharing platforms. For example, a socialnetwork post about an event directly matches a place. Similarly, user'scontacts on a social network may also be used as a source of groundtruth to validate location data.

3.5.7.2 Purchase Records

Purchase information identifies a place where a purchase occurred, and atime when the purchase occurred. These values combined with locationdata act as a form of validation and can be treated as ground truth forthe inference pipeline.

Without the location data, store activity can still be used in theinference pipeline to identify that the user has visited a place, andthe frequency of purchases can determine frequency of visits. This storeactivity acts as a behavioral roadmap of past activity as it can beindicative of future behavior in the form of places visited.

3.5.7.3 Network Data

Network data includes a network of devices that register location dataat various levels of precision. Sources of network data include mobilecarriers, network service providers, device service providers and thedata and metadata may be collected as a byproduct of core services.

As an example a mobile carrier provides cell phone service to millionsof customers via their cellular network. This network is a byproduct ofproviding core cell service registers location data because the mobiledevice is connected to an access point. Aggregating this data across allcustomers creates a density map of carrier activity associated withlocation, which the data collection system defines as Network Data. Thisnetwork data can act as a proxy of baseline location activity formillions of customers. In addition, the network data may help toidentify popular sites or more trafficked areas so that more accuratepredictions for a user's location can be made.

The network data acting as baseline location activity enables the datacollection system to identify location biases and build models tonormalize against those biases. As more sources of network data areincorporated, the models become more robust and diversified, as a singlesource may not accurately represent a population in a given geographicarea.

Network information related to WiFi base stations, such as signalstrengths, frequently-used WiFi networks, may be correlated with placeconfirmation data in order to build a history of check-in activity orempirical data. In this implementation, WiFi network features can beused as markers to identify a place. WiFi network features includeaspects such as a network name (SSID), signal strength, MAC address, IPaddress, or security settings. Features generated from a device that isassociated with a WiFi network include names of available WiFi networks,WiFi networks a device is connected to, and names of WiFi networks thatare visible to the device—all of which provide additional informationabout a person's location in relation to a place. These features act asmarkers in the inference model to determine a place.

As an example if a device is constantly connected to a certain WiFinetwork name over the span of a few days from 7 pm to 7 am, this WiFinetwork may become a marker that signifies the home of the device owner.This concept of a continuous, recurring, and/or persistent WiFi networkconnection applies outside of home, including work and school.

As another example, networks named after networking devices, such as“linksys” or “NETGEAR,” are more likely to be home networks thanbusiness networks. Also, a connection to a WiFi network is a strongindication that a person is located at or near a place that isfrequently or commonly visited. This same logic is applied to identifybusinesses, where the network name might identify an actual businessname providing the service (e.g., McDonald's WiFi, Starbucks, Barnes andNoble Customer WiFi, Best Western Inn Wireless).

4. Improving Inference Model with Machine Learning

As noted above, the inference engine may be validated with ground truthdata that maps user locations to places.

4.1 Training Data

Training data contains a list of location records or labels that can begenerated by users who carry the mobile electronic device with them innormal daily life. Each label consists of a timestamp, a reference to aplace in the database, and the user ID. The users' mobile electronicdevices are also tracking their locations. By looking up the user ID ofeach label, corresponding location readings can be retrieved. Using theretrieved location readings close to the label timestamp, one can gothrough the inference pipeline, starting from clustering, to predict thelocation of a user. The machine learning process uses the predictedplace and actual place to tune the model.

Training data can come from either direct validation data, such as usercheck-in and survey questions, or indirect validation data, such ascredit card transaction history. Indirect validation data can be used inthe same way as direct validation data, to improve ranking of placecandidates, and estimate the accuracy of an inference. Data collected ona user's mobile devices is fed to the model, and then the output of theinference model(s) is compared against the input data. Any disagreementbetween model output and desired result is sent back to the model toadjust parameters.

Each training sample consists of a feature vector and a label for everycandidate place of a cluster. For a cluster with M candidate places, thelabel is represented as a probabilityP _(y)(x _(j)), j=1,. . . ,Mwhere x_(j) is the j^(th) candidate. If x_(j)* is the correct place

${P_{y}( x_{j} )} = \{ \begin{matrix}{1,} & {j = j^{*}} \\{0,} & {j \neq j^{*}}\end{matrix} $4.2 Loss Function

The loss function is defined as the cross entropy. If the output of themodel is represented asP _(z(f) _(w) ₎(x _(j))where f is the model, and W is the model parameter (for example weightvector). The loss function is

$\begin{matrix}{{L( {y,{z( f_{W} )}} )} = {- {\sum\limits_{j = 1}^{M}{{P_{y}( x_{j} )}{\log( {P_{z{(f_{W})}}( x_{j} )} )}}}}} \\{= {- {\log( {P_{z{(f_{W})}}( x_{j^{*}} )} )}}}\end{matrix}$4.3 Optimization

Gradient descent is used to optimize the model.

The gradient of the loss function is

$\begin{matrix}{{\Delta\; W} = \frac{\partial L}{\partial W}} \\{= \frac{\partial{L( {y,{z( f_{W} )}} )}}{\partial W}} \\{= {- \frac{\partial{\log( {P_{z{(f_{W})}}( x_{j^{*}} )} )}}{\partial W}}} \\{= {\frac{\partial{f_{W}( x_{j^{*}} )}}{\partial W} + \frac{\sum\limits_{k = 1}^{M}{\exp( {{f_{W}( x_{k} )}\frac{\partial{f_{W}( x_{k} )}}{\partial W}} )}}{\sum\limits_{k = 1}^{M}{\exp( {f_{W}( x_{k} )} )}}}}\end{matrix}$Since φ_(k)=exp(s_(k)) and s_(k)=f_(W)(x_(k))

${\Delta\; W} = {{- \frac{\partial s_{j^{*}}}{\partial W}} + {\frac{1}{\sum\limits_{k = 1}^{M}\phi_{k}}{\sum\limits_{k = 1}^{M}( {\phi_{k}\frac{\partial s_{k}}{\partial W}} )}}}$More specifically

${\Delta\; W_{l}} = {{- F_{j^{*},l}} + {\frac{1}{\sum\limits_{k = 1}^{N}\phi_{k}}{\sum\limits_{k = 1}^{M}( {\phi_{k}F_{k,l}} )}}}$where F_(k,l) ^((i)) is feature l of place candidate k in instance i.

The weight vector is update with this gradientW _(l) ^(t+1) =W _(l) ^(t) +ηΔW _(l) ^(t)where η is the learning. Learning stops when the results converge.4.4 Accuracy Estimation

Some clusters are harder for the inference engine to predict than otherclusters because of a number of factors, such as location accuracy, thenumber of candidate places close to the cluster, and availability ofcertain place attributes. A classifier is trained to estimate of theaccuracy of each inference result. This estimated accuracy is used whenaggregating inference results. For example, one can apply differentweights based on the estimated probability of each inference beingcorrect.

The accuracy estimation classifier takes several features as inputincluding the average precision of locations in the cluster, the numberof place candidates nearby, whether the location is inside an indoormall, and whether the location is close to high rise buildings or in amore remote area.

This classifier is trained against validation data. This classifier doesnot impact the results of the inference engine, but it assigns aconfidence value to each inference instance. This value is used toweight the impact of each inference when aggregating results. Inferenceswith lower confidence values are weighted lower than those with higherconfidence values so that the overall result is more accurate.

5. Inference with Session Users

As discussed earlier, there are two modes of data acquisition orlocation input: continuously-tracked and session-based. What has beendescribed previously includes a continuously-tracked mode of dataacquisition. Session-based data acquisition mode is different in atleast three aspects.

-   -   1. User locations for session users are segmented, whereas user        locations for continuously-tracked users are non-stop. Location        tracking is controlled by third-party applications or devices        which integrate e.g., analytics agent. User locations are only        available when a tracking session is turned on. The result is        that the analytics server only receives location data during the        sessions, which are typically short.    -   2. User location data is collected at a higher frequency because        battery life is less of a concern when tracking is limited to        short periods of time, locations are logged with higher        frequency, typically once per second. The model will have a        better estimation of user movement.    -   3. All sensors are turned on when tracking starts, including        accelerometer, orientation, and gyroscope when available.

The difference between session-based location input and continuouslytracked location input is addressed by modifying the inference pipelinein the following ways.

5.1 Movement Detection

When user speed is higher than a threshold, the system may skip thesession and not run the inference engine. Speed is computed by dividingthe diameter of the location bounding box by the length of the session.

5.2 Skip Clustering

When the user is detected to be stationary, the system may skipclustering. The entire session is considered as a cluster. The centroidof the cluster is computed the same way as a cluster in continuoustracking mode.

5.3 Additional Features

Because session users tend to have denser location readings and sensorreadings, several features are added to the inference model to takeadvantage of additional data:

-   -   Application type data identifies the kind of application/device        that is tracking the user    -   Orientation data of the mobile electronic device    -   Base frequency of the mobile electronic device's movement. A        base frequency is computed from a Fourier transform of the        accelerometer readings, and can be generated from historical        data, such as using samples gathered over a day, week, month, or        other timeframe. The base frequency can measure how fast a body        oscillates. For example, a jogger's base frequency can be 3 Hz,        while a walker's base frequency can be 1.5 Hz.        6. Aggregation of Inference Results

After collecting information from a user's mobile device for a period oftime, and running the inference engine on the data, the results can beaggregated in different ways to serve different purposes.

6.1 Personal Location Profile

By aggregating a single user's place visit data, one can generate a userlocation profile with the user's consent which presents usefulinformation about the user's behavior and visit patterns. Suchinformation may include statistics about the user's daily commute,frequently visited places, vehicle's fuel economy, and other personallocation analytics data. From this data, one can propose alternativeroutes, offer incentives to nearby businesses, and suggest similarplaces frequented by the user in the same area.

FIG. 7 illustrates an example presentation of a personal locationprofile, which shows places visited on a weekly basis, and suggestsrelated places. While not shown in FIG. 7, timing information ortimestamps are associated with inference results to infer activitiessuch as lunch, activities after work hours, commuting, etc. As anexample, week one 710 shows a visit to McDonald's and Starbucks duringlunch for a certain amount of time (e.g., an hour and a half), spendingan hour each at Safeway, QFC, and Costco for groceries after work, andfinally refilling on gas at a Shell before returning home. Week 2's 720visits to Subway, Albertsons, Macy's, Costco, Shell, Starbucks, and TacoBell and Week 3's 730 visits to Whole Foods Market, Taco Bell, Safeway,Rite Aid and Costco can also reveal similar information such as visitduration, time of visit, venue address, proximity, etc. As a result ofthe user's personal location profile, the system can arrive at somesuggested places 740 including Wal-Mart, Trader Joe's, and Chevron.

6.2 User Location Analytics

By aggregating data of places visited across multiple users, one canproduce user location analytics for third-party mobile applicationdevelopers. Such analytics report may display, among others, how oftenusers consume a mobile application at home, at work and during commute,geographical distribution of user base, user activities before, during,and after consuming the application.

7. Conclusion

Reference throughout this specification to features, advantages, orsimilar language does not imply that all of the features and advantagesthat may be realized with the present invention should be or are in anysingle embodiment of the invention. Rather, language referring to thefeatures and advantages is understood to mean that a specific feature,advantage, or characteristic described in connection with an embodimentis included in at least one embodiment of the present invention. Thus,discussion of the features and advantages, and similar language,throughout this specification may, but do not necessarily, refer to thesame embodiment.

Furthermore, the described features, advantages, and characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. One skilled in the relevant art will recognize that theinvention can be practiced without one or more of the specific featuresor advantages of a particular embodiment. In other instances, additionalfeatures and advantages may be recognized in certain embodiments thatmay not be present in all embodiments of the invention.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof means any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, refer tothis application as a whole and not to any particular portions of thisapplication. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above Detailed Description of examples of the invention is notintended to be exhaustive or to limit the invention to the precise formdisclosed above. While specific examples for the invention are describedabove for illustrative purposes, various equivalent modifications arepossible within the scope of the invention, as those skilled in therelevant art will recognize. For example, while processes or blocks arepresented in a given order, alternative implementations may performroutines having steps, or employ systems having blocks, in a differentorder, and some processes or blocks may be deleted, moved, added,subdivided, combined, and/or modified to provide alternative orsubcombinations. Each of these processes or blocks may be implemented ina variety of different ways. Also, while processes or blocks are attimes shown as being performed in series, these processes or blocks mayinstead be performed or implemented in parallel, or may be performed atdifferent times. Further any specific numbers noted herein are onlyexamples: alternative implementations may employ differing values orranges.

The teachings of the invention provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various examples described above can be combined to providefurther implementations of the invention. Some alternativeimplementations of the invention may include not only additionalelements to those implementations noted above, but also may includefewer elements.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention.

These and other changes can be made to the invention in light of theabove Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesthe various aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C sec. 112, sixth paragraph,other aspects may likewise be embodied as a means-plus-function claim,or in other forms, such as being embodied in a computer-readable medium.(Any claims intended to be treated under 35 U.S.C. §112, ¶6 will beginwith the words “means for”, but use of the term “for” in any othercontext is not intended to invoke treatment under 35 U.S.C. §112, ¶6.)Accordingly, the applicant reserves the right to pursue additionalclaims after filing this application to pursue such additional claimforms, in either this application or in a continuing application.

We claim:
 1. A system for using location data to infer a place name of auser, wherein the system is configured to receive location readings andsensor data from multiple mobile devices wirelessly coupled to anetwork, the system comprising: at least one data storage device forstoring a place database, reference data, a sequence of locationreadings, and sensor data, wherein the place database includes placeinformation and corresponding geo-location data, wherein the referencedata links a user or a user's mobile device to a proposed place at aninstance of time, and wherein the sequence of location readings includeslatitude and longitude coordinate data and an associated time; at leastone server coupled to the data storage device, wherein the server isconfigured to: classify a sliding window of N contiguous locationreadings over the sequence of location readings as moving or not moving,wherein N is an integer number of location readings; segment thesequence of location readings into two or more location clusters;identify a centroid or sub-centroids of each of the two or more locationclusters; query the place database to identify two or more candidateplaces that are within a predetermined radius of the centroid of thecluster; extract a feature from the candidate places, wherein thefeature includes a place category, popularity, or hours of operation;and calculate a probability that the user is located at each of thecandidate places, wherein the probability is based on features of thecandidate places.
 2. The system of claim 1, wherein the reference dataincludes at least one of: a user check-in at a certain place, a placecategory, a popularity of a place, hours of operation.
 3. The system ofclaim 1, wherein the sensor data includes at least two of: acceleration,magnetic orientation, proximity, light intensity, battery status,gyroscope, or temperature.
 4. The system of claim 1, wherein thelocation readings also includes altitude, bearing, and speed of themobile device.
 5. The system of claim 1, wherein the at least one serveris further configured to rank order a predetermined number of thecandidate places for the user to validate as a confirmed location. 6.The system of claim 1, wherein the reference data is derived from atleast one of: place check-in, internet search activity, socialnetworking site activity, geo-tagged image, email, phone call, orcalendar appointment.
 7. The system of claim 1, wherein the referencedata is derived from a feature of a WiFi network, wherein the featureincludes at least one of: a network name, a signal strength, a MACaddress, an IP address, a security setting, an available WiFi network, aconnection status, a visible WiFi network, and a previously-connectedWiFi network.
 8. The system of claim 1, wherein calculating theprobability that the user is located at the candidate places is based onhours of operation for candidate places or a distance between thereference data and each of the candidate places.
 9. An apparatus forusing location data to obtain a name of a place associated with thelocation data, comprising: at least one memory; at least one radio;input/output components; at least one processor coupled to the memory,radio, and input/output components, wherein the apparatus furthercomprises: means for storing a place database, reference data, asequence of location readings and sensor data, wherein the placedatabase includes place information and corresponding geo-location data,wherein the reference data includes a user and a proposed place at aninstance of time, and wherein the sequence of location readings includeslatitude and longitude coordinate data and an associated time; means forfiltering location readings to produce filtered location data; means foridentifying a location from the filtered location data; means foridentifying multiple candidate places based on the identified location;and means for calculating a probability that the user is located at themultiple candidate places.
 10. The apparatus of claim 9, wherein themeans for filtering employs a sliding window of N contiguous locationreadings over the sequence of location readings as moving or not moving,wherein N is an integer number of location readings.
 11. The apparatusof claim 10, further comprising means for segmenting the sequence oflocation readings into two or more location clusters, and wherein themeans for identifying includes identifying a centroid of each of the twoor more location clusters.
 12. The apparatus of claim 9, wherein themeans for identifying multiple candidate locations includes querying aplace database to identify two or more candidate places that are withina predetermined radius of a centroid computed from the identifiedlocation.
 13. The apparatus of claim 9, further comprising means forrank ordering a predetermined number of the multiple candidate placesfor the user to validate as a confirmed location.
 14. The apparatus ofclaim 9, wherein the reference data is derived from at least one of:internet search activity, social networking site activity, geo-taggedimage, an email, a phone call, a calendar appointment, or a placecheck-in.
 15. The apparatus of claim 9, wherein the means forcalculating the probability also factors in hours of operation of themultiple candidate places.
 16. A non-transitory computer readablestorage medium storing instructions for using validated place data toinfer a location of a user, the computer readable storage mediumcomprising: instructions for analyzing a series of location datareceived from a mobile device— to identify a stop time when the mobiledevice has stopped for longer than a threshold time, and to identify alocation associated with the stop time; instructions for querying aplace name database to identify multiple candidate place names that arewithin a predetermined radius of the identified location; instructionsfor obtaining attributes of the stop time and attributes of the multiplecandidate place names; and instructions for identifying one of themultiple candidate place names as a likely place name for the identifiedlocation at the stop time.
 17. The computer readable storage medium ofclaim 16, further comprising instructions for storing the place namedatabase, a reference data, the series of location data, and sensordata, wherein the place name database includes place information andcorresponding geo-location data, wherein the reference data links a userto a proposed place at an instance of time, and wherein the series oflocation data includes latitude and longitude coordinate data and anassociated time.
 18. The computer readable storage medium of claim 16,further comprising: instructions for classifying a sliding window of Ncontiguous location data over the series of location data as moving ornot moving, wherein N is an integer number of location data;instructions for segmenting the series of location data into two or morelocation clusters based on whether the sliding window is classified asmoving or not moving; and instructions for identifying a place name foreach of the two or more location clusters.
 19. A method of inferring alocation of a user using location readings and sensor data from a mobiledevice wirelessly coupled to a network, the method comprising: receivingmultiple location readings, wherein each location reading includeslatitude and longitude coordinate data, and wherein each locationreading has an associated time and an estimated accuracy; filtering thelocation readings to remove noise from the location readings;determining if the mobile device is stationary from the filteredlocation readings; temporally clustering the filtered location readingsto identify a time and location of a stop; and identifying a significantstop from the temporally clustered location readings.
 20. The method ofclaim 19, further comprising purging one or more location readings withan estimated accuracy lower than an accuracy threshold to produceresulting location data, wherein the purging of one or more locationreadings includes location readings from cell-tower triangulation. 21.The method of claim 19, further comprising classifying a sliding windowas moving or not moving by: computing a duration of the sliding windowof N contiguous location readings; computing a great-circle distance ofthe sliding window of N contiguous location readings, wherein thegreat-circle distance is based on a minimum latitude, a minimumlongitude, a maximum latitude, and a maximum longitude; and computing aspeed of the sliding window of N contiguous location readings, whereincomputing includes dividing the great circle distance by the duration.22. The method of 19, further comprising classifying a sliding window asmoving or not moving by defining the sliding window as moving when thegreat-circle distance of the portion of the sequence of locationreadings is above a distance threshold.
 23. The method of claim 19,further comprising: for each sliding window of N contiguous locationreadings: computing a maximum distance between any of the locationreadings of the sliding window of N location contiguous readings; whenthe maximum distance is equal to or greater than a distance threshold,classifying the sliding window of N contiguous location readings asmoving; and when the maximum distance is less than a distance threshold,classifying the sliding window of N contiguous location readings as notmoving.
 24. The method of claim 19, wherein sensor data includesaccelerometer data, and wherein classifying the sliding window as movingor not moving further includes applying a Fourier transform to theaccelerometer data in order to determine a base frequency.
 25. Themethod of claim 19, further comprising: merging two or more clusters oflocation data when a centroid of each of the two or more locationclusters is below a centroid threshold.
 26. The method of claim 19,further comprising receiving reference data, wherein the reference datalinks the user to a proposed place at an instance of time, and whereinthe reference data includes a place confirmation at a particular place.27. The method of claim 26, further comprising calculating a probabilitythat the user is located at multiple candidate places, wherein theprobability factors in a distance between reference data and each of themultiple candidate places; and extracting a feature of the multiplecandidate places, wherein the feature includes a place category andhours of operation.
 28. The method of claim 19, further comprisingreceiving reference data, wherein the reference data links the user to aproposed place at an instance of time, and wherein the reference data isderived from at least one of: place check-in, internet search activity,social networking site activity, geo-tagged image, email, phone call, orcalendar appointment.